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Continously ingest and deliver both streaming and batch change data from 100s of sources using Estuary's custom no-code connectors.
- <100ms Data pipelines
- 100+ Connectors
- 2-5x less than batch ELT
Amazon DynamoDB connector details
The Estuary DynamoDB connector streams real-time data from Amazon DynamoDB tables using DynamoDB Streams. Once streams are enabled on your tables, the connector continuously captures inserts, updates, and deletes into Flow collections for seamless downstream processing. It supports secure AWS authentication, scalable backfills, and configurable read capacity for efficient ingestion at any scale.
- Continuous CDC streaming via DynamoDB Streams
- Captures new and old images for full change context
- Works with AWS IAM-based authentication
- Configurable RCU allocation for controlled backfill performance
- Supports multiple tables and regions within a single setup
HIGH THROUGHPUT
Distributed event-driven architecture enable boundless scaling with exactly-once semantics.
DURABLE REPLICATION
Cloud storage backed CDC w/ heart beats ensures reliability, even if your destination is down.
REAL-TIME INGESTION
Capture and relay every insert, update, and delete in milliseconds.
Real-timehigh throughput
Point a connector and replicate changes from Amazon DynamoDB in <100ms. Leverage high-availability, high-throughput Change Data Capture.Or choose from 100s of batch and real-time connectors to move and transform data using ELT and ETL.
- Ensure your Amazon DynamoDB insights always reflect the latest data by connecting your databases to Amazon DynamoDB with change data capture.
- Or connect critical SaaS apps to Amazon DynamoDB with real-time data pipelines.
Don't see a connector?Request and our team will get back to you in 24 hours
Pipelines as fast as Kafka, easy as managed ELT/ETL, cheaper than building it.
Feature Comparison
| Estuary | Batch ELT/ETL | DIY Python | KAFKA | |
|---|---|---|---|---|
| Price | $ | $$-$$$$ | $-$$$$ | $-$$$$ |
| Speed | <100ms | 5min+ | Varies | <100ms |
| Ease | Analysts can manage | Analysts can manage | Data Engineer | Senior Data Engineer |
| Scale |
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